Surveillance systems in malaria endemic-countries are needed to capture information on intervention coverage and changes in malaria transmission, infection, and disease. Importanfiy, surveillance data must be effectively communicated to policy-makers to inform future intervention strategies. However, the capacity to conduct high-quality malaria surveillance is currenfiy inadequate in much of Africa and the existing health information system is insufficient for monitoring progress in malaria control. Data on vector behavior and transmission intensity are not roufinely collected. Morbidity and mortality data collected at health facilities may be biased and are often incomplete, inaccurate, and largely rely on clinical diagnosis in the absence of laboratory confirmation. Community surveys are currenfiy the most robust strategy for malaria surveillance, but are expensive and logistically challenging, conducted infrequently with limited geographic coverage, and not comprehensive enough to fully capture the dynamics of transmission, infecfion, and disease. Identifying the opfimal methods of gathering reliable data for roufine malaria surveillance is essenfial for improving our understanding of malaria epidemiology and providing an evidence base for maximizing the impact of control intervenfions. For this project comprehensive malaria surveillance studies will be conducted at 3 senfinel sites with widely varied epidemiology to collect data on measures of transmission intensity, infection and disease and identify opfimal methods for surveillance. Surveillance activities will then be streamlined and expand to 6 sentinel sites to measure the impact of key malaria control intervenfions on malaria transmission, infection, and disease. Our specific aims will be: 1) to identify opfimal strategies for malaria surveillance in Uganda by comparing different methodologies at mulfiple sites with varied transmission intensity, 2) to estimate the impact of key malaria control interventions on measures of transmission intensity, infecfion, and disease using surveillance data at mulfiple sites in Uganda, and 3) to conduct an economic evaluation of malaria control interventions to identify the optimal coverage levels and mix of interventions at mulfiple sites in Uganda.